Resource management for traffic imbalance problem in green cognitive radio networks

Srivastava, Akanksha; Kaur, Gurjit

doi:10.1016/j.phycom.2021.101437

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Gupta et al proposed an auction framework and Stackelberg game‐oriented optimal network's resource selection technique to achieve a maximum payoff and acceptable interference power restraint in CRNs in Reference 30. In Reference 31, to avoid multiple SUs accessing the same channel to reduce energy effective (EE), Srivastava et al proposed a channel decision model based on queuing priority for two‐channel selection strategies based on probability and sensing, respectively. In Reference 32, an optimal spectrum resource allocation (SRA) scheme was proposed by Zhang et al, in which the PU selects the best subchannel to transmit data.…”

Section: Introductionmentioning

confidence: 99%

Joint spectrum sensing and resource allocation against byzantine attack in overlay cognitive radio networks

Gan

et al. 2022

Trans Emerging Tel Tech

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Cognitive radio (CR) technology has emerged as a viable solution to solve the dilemma of spectrum scarcity and underutilization. It allows secondary users (SUs) to opportunistically access the primary user (PU) channel. Cooperative spectrum sensing (CSS) further improves the accuracy of detecting spectrum availability by exploiting spatial diversity gain. However, the open nature of CR networks (CRNs) makes CSS process be prone to Byzantine attack, resulting in the excessive interference to the PU and the waste of spectrum resources. To this end, we propose a joint spectrum sensing and resource allocation scheme to defend against Byzantine attack. Specifically, we develop a probabilistic Byzantine attack model to characterize attack behaviors from malicious users (MUs) and evaluate its negative impact on the CSS performance. Then, the delivery evaluation mechanism is designed to evaluate the sensing performance of SUs and distinguish between normal users and MUs. Based on this, we propose a selection-majority algorithm to improve CSS performance by exploiting sensing reports from MUs. To further solve the Byzantine attack, we propose a spectrum resource allocation algorithm, which allocates corresponding spectrum resources according to the historical behavior of SUs. By reducing the resources allocated to poorly performing SUs, this scheme incentivizes them to report correct sensing results and drive MUs to stop attacking or leave CRN. Extensive simulation results corroborate the correctness and effectiveness of our theoretical analysis and show that our proposed scheme can effectively resist Byzantine attack.

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Section: Introductionmentioning

confidence: 99%

Joint spectrum sensing and resource allocation against byzantine attack in overlay cognitive radio networks

Gan

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…This, then, instantly allows transmitting terminals to opportunistically make use of idle spectrum while avoiding the occupied ones. It is a technology that advocates the concept of dynamic spectrum access, contrary to the existing fixed spectrum access [9][10][11]. CRN improves spectrum utilization by allowing unauthorized users to access the allocated spectrum without interfering with the authorized user, that is, Primary User (PU).…”

Section: Introductionmentioning

confidence: 99%

Autocorrelation Based White Space Detection in Energy Harvesting Cognitive Radio Network

Ojo

Adeyemo

Omowaiye

et al. 2021

IJEEI

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Accurate detection of White Space (WS) is of paramount importance in a Cognitive Radio Network (CRN) to prevent authorized users from harmful interference. However, channel impairment such as multipath fading and shadowing affects accurate detection of WS resulting in interference. The Existing Feature Detection (EFD) technique used to address the problem is faced with computational complexity and synchronization resulting in long sensing time, bandwidth inefficiency, energy constrain and poor detection rate. Hence, this paper proposes autocorrelation based multiple antenna with energy harvesting for WS detection in a CRN using Radio Frequency (RF) energy harvesting and autocorrelation of the received signal with a modified Equal Gain Combiner (mEGC). Antenna Switching (AS) RF energy harvesting with mEGC are used to harvest energy and information from the received PU signal in a multiple antenna configuration. Autocorrelation is then obtained and compared with the set threshold of zero to determine the presence or absence of WS. The proposed technique is evaluated using Spectral Efficiency (SE), Probability of Detection (PD) and Sensing Time (ST) by comparing with EFD technique. The results obtained revealed that the proposed technique shows better performance than EFD.

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Enhancing spectral efficiency of green metric cognitive radio network using an adaptive optimization and communication protocol

Kumar,

Kumari

2024

Int J Communication

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SummaryInformation technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy.

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Resource management for traffic imbalance problem in green cognitive radio networks

Cited by 10 publications

References 26 publications

Joint spectrum sensing and resource allocation against byzantine attack in overlay cognitive radio networks

Joint spectrum sensing and resource allocation against byzantine attack in overlay cognitive radio networks

Autocorrelation Based White Space Detection in Energy Harvesting Cognitive Radio Network

Enhancing spectral efficiency of green metric cognitive radio network using an adaptive optimization and communication protocol

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